Seamless analysis of your PyTorch models (RAM usage, FLOPs, MACs, receptive field, etc.)
APACHE-2.0 License
Presents comprehensive benchmarks of XLA-compatible pre-trained models in Keras.
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
torchview: visualize pytorch models
.NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Lea...
View model summaries in PyTorch!
In this repository, I will share some useful notes and references about deploying deep learning-b...
Interpretability Methods for tf.keras models with Tensorflow 2.x
A collection of computer vision pre-trained models.
[5 FPS - 150 FPS] Learning Deep Features for One-Class Classification (AnomalyDetection). Corresp...
Shape and dimension inference (Keras-like) for PyTorch layers and neural networks
Model summary in PyTorch similar to `model.summary()` in Keras
A curated list of dedicated resources and applications
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g...
Header-only library for using Keras (TensorFlow) models in C++.
PyTorch and TensorFlow/Keras image models with automatic weight conversions and equal API/impleme...